Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 24

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f7020af2d68>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 16

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f701e2eb048>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    
    real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='real_input')
    z = tf.placeholder(tf.float32, (None, z_dim), name='z_input')
    learning_rate = tf.placeholder(tf.float32, None, name='lr')

    return real, z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/runpy.py", line 184, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/ipykernel/kernelapp.py", line 477, in start\n    ioloop.IOLoop.instance().start()', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/ipykernel/ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2808, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-cdb1f4eba039>", line 24, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/pedro/Documents/Projetos/NanoDegree_DeepLearning/dlnd-projects/face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/pedro/Documents/Projetos/NanoDegree_DeepLearning/dlnd-projects/face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/pedro/Documents/Projetos/NanoDegree_DeepLearning/dlnd-projects/face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/pedro/Documents/Projetos/NanoDegree_DeepLearning/dlnd-projects/face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py", line 170, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py", line 139, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/home/pedro/anaconda3/envs/tfgpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py", line 96, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False, alpha=0.02):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(x1, 128, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d(x2, 256, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=True)
        x3 = tf.maximum(alpha * x3, x3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(x3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [9]:
def generator(z, out_channel_dim, is_train=True, alpha=0.02):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    reuse = not is_train
    with tf.variable_scope('generator', reuse=reuse):
        # First fully connected layer
        x1 = tf.layers.dense(z, (7*7*256))

        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, padding='same')
        # 28x28x3 now
        
        
        
        return tf.tanh(logits)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [10]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.02):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [11]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [12]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [21]:
import datetime

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    ## THIS METHOD IS HEAVILY INSPIRED IN THE TRAIN METHOD FROM DCGAN-SVHN LESSON
    show_every_n_steps, print_every, n_images = 100, 50, 25
    
    # Set the alpha for leaky ReLu's
    alpha = 0.2
    
    
    real_input, z_input, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    
    desc_loss, gen_loss = model_loss(real_input, z_input, data_shape[3], alpha=0.1)
    
    d_train, g_train = model_opt(desc_loss, gen_loss, lr, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            batch_count = 0
            for batch_images in get_batches(batch_size):
                batch_count += 1
                
                # Re-scale images to be in the range of -1 to 1
                batch_images *= 2
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_train, feed_dict={real_input: batch_images, 
                                                 z_input: batch_z, 
                                                 lr: learning_rate})
                
                _ = sess.run(g_train, feed_dict={z_input: batch_z, 
                                                 real_input: batch_images, 
                                                 lr: learning_rate})
                
                if batch_count % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = desc_loss.eval({z_input: batch_z, real_input: batch_images})
                    train_loss_g = gen_loss.eval({z_input: batch_z})
                    date_time = datetime.datetime.now()
                    

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}...".format(train_loss_g),
                          "TS: {}".format(date_time))
                
                if batch_count % show_every_n_steps == 0:
                    show_generator_output(sess, n_images, z_input, data_shape[3], data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [23]:
batch_size = 64
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.3813... Generator Loss: 0.4363... TS: 2017-08-23 15:11:06.963042
Epoch 1/2... Discriminator Loss: 0.6235... Generator Loss: 1.0825... TS: 2017-08-23 15:11:10.184500
Epoch 1/2... Discriminator Loss: 0.4991... Generator Loss: 2.0062... TS: 2017-08-23 15:11:13.873351
Epoch 1/2... Discriminator Loss: 0.4854... Generator Loss: 1.4406... TS: 2017-08-23 15:11:17.170387
Epoch 1/2... Discriminator Loss: 0.5914... Generator Loss: 1.1314... TS: 2017-08-23 15:11:20.895323
Epoch 1/2... Discriminator Loss: 0.4244... Generator Loss: 2.1609... TS: 2017-08-23 15:11:24.189452
Epoch 1/2... Discriminator Loss: 0.5507... Generator Loss: 1.1899... TS: 2017-08-23 15:11:27.846509
Epoch 1/2... Discriminator Loss: 0.6258... Generator Loss: 3.2131... TS: 2017-08-23 15:11:31.113002
Epoch 1/2... Discriminator Loss: 0.4753... Generator Loss: 1.4474... TS: 2017-08-23 15:11:34.747318
Epoch 1/2... Discriminator Loss: 0.6890... Generator Loss: 1.0548... TS: 2017-08-23 15:11:38.037713
Epoch 1/2... Discriminator Loss: 0.7967... Generator Loss: 0.8617... TS: 2017-08-23 15:11:41.749407
Epoch 1/2... Discriminator Loss: 0.6565... Generator Loss: 1.3462... TS: 2017-08-23 15:11:45.032572
Epoch 1/2... Discriminator Loss: 1.0341... Generator Loss: 0.5731... TS: 2017-08-23 15:11:48.704222
Epoch 1/2... Discriminator Loss: 0.6003... Generator Loss: 1.1768... TS: 2017-08-23 15:11:52.003872
Epoch 1/2... Discriminator Loss: 0.4457... Generator Loss: 1.7468... TS: 2017-08-23 15:11:55.641821
Epoch 1/2... Discriminator Loss: 0.7658... Generator Loss: 0.9082... TS: 2017-08-23 15:11:58.944207
Epoch 1/2... Discriminator Loss: 0.5717... Generator Loss: 1.1691... TS: 2017-08-23 15:12:02.619688
Epoch 1/2... Discriminator Loss: 0.4485... Generator Loss: 1.4629... TS: 2017-08-23 15:12:05.897626
Epoch 2/2... Discriminator Loss: 0.5688... Generator Loss: 1.3062... TS: 2017-08-23 15:12:11.957604
Epoch 2/2... Discriminator Loss: 0.7731... Generator Loss: 0.8750... TS: 2017-08-23 15:12:15.228289
Epoch 2/2... Discriminator Loss: 0.3637... Generator Loss: 1.9087... TS: 2017-08-23 15:12:18.903659
Epoch 2/2... Discriminator Loss: 0.3031... Generator Loss: 2.0851... TS: 2017-08-23 15:12:22.192747
Epoch 2/2... Discriminator Loss: 0.5229... Generator Loss: 1.2836... TS: 2017-08-23 15:12:25.804492
Epoch 2/2... Discriminator Loss: 0.6507... Generator Loss: 1.0533... TS: 2017-08-23 15:12:29.054662
Epoch 2/2... Discriminator Loss: 0.7842... Generator Loss: 2.6637... TS: 2017-08-23 15:12:32.726829
Epoch 2/2... Discriminator Loss: 0.7757... Generator Loss: 0.8195... TS: 2017-08-23 15:12:35.964793
Epoch 2/2... Discriminator Loss: 0.4674... Generator Loss: 1.4899... TS: 2017-08-23 15:12:39.581942
Epoch 2/2... Discriminator Loss: 0.2659... Generator Loss: 2.4000... TS: 2017-08-23 15:12:42.819158
Epoch 2/2... Discriminator Loss: 0.8011... Generator Loss: 0.8888... TS: 2017-08-23 15:12:46.958245
Epoch 2/2... Discriminator Loss: 0.4240... Generator Loss: 1.5132... TS: 2017-08-23 15:12:50.184644
Epoch 2/2... Discriminator Loss: 0.4060... Generator Loss: 1.6264... TS: 2017-08-23 15:12:53.816869
Epoch 2/2... Discriminator Loss: 0.3139... Generator Loss: 1.7960... TS: 2017-08-23 15:12:57.105676
Epoch 2/2... Discriminator Loss: 0.4808... Generator Loss: 2.6300... TS: 2017-08-23 15:13:00.756296
Epoch 2/2... Discriminator Loss: 0.9493... Generator Loss: 0.7960... TS: 2017-08-23 15:13:04.005661
Epoch 2/2... Discriminator Loss: 0.3688... Generator Loss: 1.7776... TS: 2017-08-23 15:13:07.652711
Epoch 2/2... Discriminator Loss: 0.8889... Generator Loss: 0.7705... TS: 2017-08-23 15:13:10.910057

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [25]:
# Hyper parameters base on https://arxiv.org/pdf/1511.06434.pdf
batch_size = 32
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.0180... Generator Loss: 0.5806... TS: 2017-08-23 15:28:20.220365
Epoch 1/2... Discriminator Loss: 0.7748... Generator Loss: 1.0383... TS: 2017-08-23 15:28:23.593653
Epoch 1/2... Discriminator Loss: 0.4199... Generator Loss: 2.2135... TS: 2017-08-23 15:28:27.159331
Epoch 1/2... Discriminator Loss: 0.7336... Generator Loss: 1.3211... TS: 2017-08-23 15:28:30.582129
Epoch 1/2... Discriminator Loss: 0.9381... Generator Loss: 0.7195... TS: 2017-08-23 15:28:34.157552
Epoch 1/2... Discriminator Loss: 0.9049... Generator Loss: 0.8790... TS: 2017-08-23 15:28:37.453513
Epoch 1/2... Discriminator Loss: 0.6581... Generator Loss: 1.0735... TS: 2017-08-23 15:28:41.113828
Epoch 1/2... Discriminator Loss: 0.9587... Generator Loss: 0.7175... TS: 2017-08-23 15:28:44.424983
Epoch 1/2... Discriminator Loss: 0.6865... Generator Loss: 1.2903... TS: 2017-08-23 15:28:48.148708
Epoch 1/2... Discriminator Loss: 0.9826... Generator Loss: 0.6988... TS: 2017-08-23 15:28:51.426340
Epoch 1/2... Discriminator Loss: 1.2590... Generator Loss: 0.4650... TS: 2017-08-23 15:28:55.093868
Epoch 1/2... Discriminator Loss: 0.9978... Generator Loss: 0.6449... TS: 2017-08-23 15:28:58.358338
Epoch 1/2... Discriminator Loss: 0.7823... Generator Loss: 0.8337... TS: 2017-08-23 15:29:02.079047
Epoch 1/2... Discriminator Loss: 0.9232... Generator Loss: 0.7047... TS: 2017-08-23 15:29:05.396513
Epoch 1/2... Discriminator Loss: 0.9961... Generator Loss: 0.7512... TS: 2017-08-23 15:29:09.090069
Epoch 1/2... Discriminator Loss: 0.6607... Generator Loss: 1.2007... TS: 2017-08-23 15:29:12.404107
Epoch 1/2... Discriminator Loss: 0.8015... Generator Loss: 1.5186... TS: 2017-08-23 15:29:16.061729
Epoch 1/2... Discriminator Loss: 0.6665... Generator Loss: 1.1189... TS: 2017-08-23 15:29:19.265384
Epoch 1/2... Discriminator Loss: 1.9701... Generator Loss: 0.2118... TS: 2017-08-23 15:29:22.929579
Epoch 1/2... Discriminator Loss: 0.6689... Generator Loss: 1.2956... TS: 2017-08-23 15:29:26.127652
Epoch 1/2... Discriminator Loss: 0.9934... Generator Loss: 1.1778... TS: 2017-08-23 15:29:29.688614
Epoch 1/2... Discriminator Loss: 0.6496... Generator Loss: 1.5101... TS: 2017-08-23 15:29:32.936954
Epoch 1/2... Discriminator Loss: 0.7882... Generator Loss: 0.8979... TS: 2017-08-23 15:29:36.742085
Epoch 1/2... Discriminator Loss: 1.1315... Generator Loss: 0.5357... TS: 2017-08-23 15:29:40.070304
Epoch 1/2... Discriminator Loss: 0.7941... Generator Loss: 0.8272... TS: 2017-08-23 15:29:43.783834
Epoch 1/2... Discriminator Loss: 0.7826... Generator Loss: 0.9959... TS: 2017-08-23 15:29:46.963223
Epoch 1/2... Discriminator Loss: 1.6248... Generator Loss: 0.2733... TS: 2017-08-23 15:29:50.624912
Epoch 1/2... Discriminator Loss: 0.8488... Generator Loss: 0.7652... TS: 2017-08-23 15:29:53.857227
Epoch 1/2... Discriminator Loss: 0.3615... Generator Loss: 2.2323... TS: 2017-08-23 15:29:57.468371
Epoch 1/2... Discriminator Loss: 0.8700... Generator Loss: 1.0382... TS: 2017-08-23 15:30:00.757148
Epoch 1/2... Discriminator Loss: 0.9777... Generator Loss: 0.6065... TS: 2017-08-23 15:30:04.466237
Epoch 1/2... Discriminator Loss: 0.7584... Generator Loss: 0.9532... TS: 2017-08-23 15:30:07.641048
Epoch 1/2... Discriminator Loss: 0.7616... Generator Loss: 1.0881... TS: 2017-08-23 15:30:11.346165
Epoch 1/2... Discriminator Loss: 0.7098... Generator Loss: 0.9943... TS: 2017-08-23 15:30:14.635102
Epoch 1/2... Discriminator Loss: 0.6407... Generator Loss: 0.9654... TS: 2017-08-23 15:30:18.470649
Epoch 1/2... Discriminator Loss: 0.9297... Generator Loss: 0.6835... TS: 2017-08-23 15:30:21.756049
Epoch 1/2... Discriminator Loss: 0.9039... Generator Loss: 0.7151... TS: 2017-08-23 15:30:25.530059
Epoch 1/2... Discriminator Loss: 1.2178... Generator Loss: 0.4545... TS: 2017-08-23 15:30:28.989352
Epoch 1/2... Discriminator Loss: 0.8884... Generator Loss: 0.7515... TS: 2017-08-23 15:30:32.671874
Epoch 1/2... Discriminator Loss: 0.4789... Generator Loss: 1.5842... TS: 2017-08-23 15:30:35.923188
Epoch 1/2... Discriminator Loss: 0.7684... Generator Loss: 1.0180... TS: 2017-08-23 15:30:39.878677
Epoch 1/2... Discriminator Loss: 0.6076... Generator Loss: 1.2735... TS: 2017-08-23 15:30:43.154129
Epoch 1/2... Discriminator Loss: 1.2069... Generator Loss: 0.6371... TS: 2017-08-23 15:30:46.732192
Epoch 1/2... Discriminator Loss: 0.6405... Generator Loss: 1.2322... TS: 2017-08-23 15:30:50.067319
Epoch 1/2... Discriminator Loss: 0.4719... Generator Loss: 1.2913... TS: 2017-08-23 15:30:53.670618
Epoch 1/2... Discriminator Loss: 0.3011... Generator Loss: 1.6816... TS: 2017-08-23 15:30:56.935194
Epoch 1/2... Discriminator Loss: 1.6958... Generator Loss: 0.2700... TS: 2017-08-23 15:31:00.624182
Epoch 1/2... Discriminator Loss: 1.0276... Generator Loss: 0.6218... TS: 2017-08-23 15:31:03.896458
Epoch 1/2... Discriminator Loss: 0.5564... Generator Loss: 1.2149... TS: 2017-08-23 15:31:07.592083
Epoch 1/2... Discriminator Loss: 0.4650... Generator Loss: 2.3419... TS: 2017-08-23 15:31:10.873451
Epoch 1/2... Discriminator Loss: 0.7915... Generator Loss: 0.8973... TS: 2017-08-23 15:31:14.553736
Epoch 1/2... Discriminator Loss: 0.6155... Generator Loss: 1.1860... TS: 2017-08-23 15:31:17.798053
Epoch 1/2... Discriminator Loss: 1.1000... Generator Loss: 1.0569... TS: 2017-08-23 15:31:21.518469
Epoch 1/2... Discriminator Loss: 1.1412... Generator Loss: 0.5442... TS: 2017-08-23 15:31:24.828284
Epoch 1/2... Discriminator Loss: 0.6275... Generator Loss: 1.1432... TS: 2017-08-23 15:31:28.505087
Epoch 1/2... Discriminator Loss: 1.9085... Generator Loss: 0.2111... TS: 2017-08-23 15:31:31.911913
Epoch 1/2... Discriminator Loss: 1.0591... Generator Loss: 0.5909... TS: 2017-08-23 15:31:35.634734
Epoch 1/2... Discriminator Loss: 0.4997... Generator Loss: 1.5999... TS: 2017-08-23 15:31:38.945641
Epoch 1/2... Discriminator Loss: 0.5789... Generator Loss: 1.3640... TS: 2017-08-23 15:31:42.717786
Epoch 1/2... Discriminator Loss: 1.8046... Generator Loss: 0.2478... TS: 2017-08-23 15:31:46.006991
Epoch 1/2... Discriminator Loss: 0.8901... Generator Loss: 1.0386... TS: 2017-08-23 15:31:49.687568
Epoch 1/2... Discriminator Loss: 0.6359... Generator Loss: 1.0042... TS: 2017-08-23 15:31:53.026777
Epoch 1/2... Discriminator Loss: 0.2692... Generator Loss: 1.8903... TS: 2017-08-23 15:31:56.713677
Epoch 1/2... Discriminator Loss: 0.8857... Generator Loss: 0.8126... TS: 2017-08-23 15:31:59.935361
Epoch 1/2... Discriminator Loss: 0.7666... Generator Loss: 1.3372... TS: 2017-08-23 15:32:03.617534
Epoch 1/2... Discriminator Loss: 1.2631... Generator Loss: 0.4059... TS: 2017-08-23 15:32:06.782005
Epoch 1/2... Discriminator Loss: 0.4980... Generator Loss: 1.6239... TS: 2017-08-23 15:32:10.436230
Epoch 1/2... Discriminator Loss: 0.6533... Generator Loss: 1.1872... TS: 2017-08-23 15:32:13.719248
Epoch 1/2... Discriminator Loss: 0.4713... Generator Loss: 1.4643... TS: 2017-08-23 15:32:17.402200
Epoch 1/2... Discriminator Loss: 0.3261... Generator Loss: 1.9928... TS: 2017-08-23 15:32:20.651298
Epoch 1/2... Discriminator Loss: 1.0973... Generator Loss: 0.6268... TS: 2017-08-23 15:32:24.327375
Epoch 1/2... Discriminator Loss: 2.0391... Generator Loss: 0.1912... TS: 2017-08-23 15:32:27.601507
Epoch 1/2... Discriminator Loss: 0.6220... Generator Loss: 0.9402... TS: 2017-08-23 15:32:31.282718
Epoch 1/2... Discriminator Loss: 1.6240... Generator Loss: 0.3243... TS: 2017-08-23 15:32:34.581206
Epoch 1/2... Discriminator Loss: 0.2673... Generator Loss: 2.0389... TS: 2017-08-23 15:32:38.305608
Epoch 1/2... Discriminator Loss: 0.5037... Generator Loss: 1.1983... TS: 2017-08-23 15:32:41.459884
Epoch 1/2... Discriminator Loss: 0.3834... Generator Loss: 1.7625... TS: 2017-08-23 15:32:45.208455
Epoch 1/2... Discriminator Loss: 1.0636... Generator Loss: 0.5498... TS: 2017-08-23 15:32:48.491996
Epoch 1/2... Discriminator Loss: 1.7459... Generator Loss: 0.2775... TS: 2017-08-23 15:32:52.188001
Epoch 1/2... Discriminator Loss: 1.8258... Generator Loss: 0.2580... TS: 2017-08-23 15:32:55.484176
Epoch 1/2... Discriminator Loss: 0.5082... Generator Loss: 2.4493... TS: 2017-08-23 15:32:59.160646
Epoch 1/2... Discriminator Loss: 0.7804... Generator Loss: 0.9526... TS: 2017-08-23 15:33:02.526388
Epoch 1/2... Discriminator Loss: 0.8235... Generator Loss: 0.7230... TS: 2017-08-23 15:33:06.239800
Epoch 1/2... Discriminator Loss: 0.0949... Generator Loss: 3.3895... TS: 2017-08-23 15:33:09.445395
Epoch 1/2... Discriminator Loss: 1.6230... Generator Loss: 4.2337... TS: 2017-08-23 15:33:13.117041
Epoch 1/2... Discriminator Loss: 1.4395... Generator Loss: 0.4455... TS: 2017-08-23 15:33:16.355859
Epoch 1/2... Discriminator Loss: 1.2301... Generator Loss: 0.4860... TS: 2017-08-23 15:33:20.077512
Epoch 1/2... Discriminator Loss: 0.4025... Generator Loss: 1.6592... TS: 2017-08-23 15:33:23.430375
Epoch 1/2... Discriminator Loss: 1.1247... Generator Loss: 0.5612... TS: 2017-08-23 15:33:27.147632
Epoch 1/2... Discriminator Loss: 1.2068... Generator Loss: 0.5107... TS: 2017-08-23 15:33:30.363703
Epoch 1/2... Discriminator Loss: 0.2298... Generator Loss: 2.2051... TS: 2017-08-23 15:33:34.144648
Epoch 1/2... Discriminator Loss: 0.5179... Generator Loss: 1.3909... TS: 2017-08-23 15:33:37.420486
Epoch 1/2... Discriminator Loss: 0.1036... Generator Loss: 3.4901... TS: 2017-08-23 15:33:41.099520
Epoch 1/2... Discriminator Loss: 0.8596... Generator Loss: 0.8044... TS: 2017-08-23 15:33:44.376231
Epoch 1/2... Discriminator Loss: 1.3636... Generator Loss: 0.4036... TS: 2017-08-23 15:33:48.061318
Epoch 1/2... Discriminator Loss: 1.4071... Generator Loss: 0.4629... TS: 2017-08-23 15:33:51.290849
Epoch 1/2... Discriminator Loss: 1.7539... Generator Loss: 0.2587... TS: 2017-08-23 15:33:54.995501
Epoch 1/2... Discriminator Loss: 0.3019... Generator Loss: 2.1170... TS: 2017-08-23 15:33:58.303901
Epoch 1/2... Discriminator Loss: 0.3866... Generator Loss: 2.2841... TS: 2017-08-23 15:34:02.045660
Epoch 1/2... Discriminator Loss: 0.7078... Generator Loss: 0.9474... TS: 2017-08-23 15:34:05.346196
Epoch 1/2... Discriminator Loss: 0.7344... Generator Loss: 0.9143... TS: 2017-08-23 15:34:09.013543
Epoch 1/2... Discriminator Loss: 1.1862... Generator Loss: 0.4893... TS: 2017-08-23 15:34:12.449778
Epoch 1/2... Discriminator Loss: 0.4748... Generator Loss: 1.3844... TS: 2017-08-23 15:34:16.129095
Epoch 1/2... Discriminator Loss: 2.2733... Generator Loss: 0.1926... TS: 2017-08-23 15:34:19.341176
Epoch 1/2... Discriminator Loss: 0.6754... Generator Loss: 1.0432... TS: 2017-08-23 15:34:23.103822
Epoch 1/2... Discriminator Loss: 0.1026... Generator Loss: 3.6055... TS: 2017-08-23 15:34:26.385742
Epoch 1/2... Discriminator Loss: 0.5433... Generator Loss: 4.6014... TS: 2017-08-23 15:34:30.013951
Epoch 1/2... Discriminator Loss: 0.6414... Generator Loss: 1.1380... TS: 2017-08-23 15:34:33.285059
Epoch 1/2... Discriminator Loss: 3.0816... Generator Loss: 0.0795... TS: 2017-08-23 15:34:36.947225
Epoch 1/2... Discriminator Loss: 1.1704... Generator Loss: 0.5493... TS: 2017-08-23 15:34:40.225860
Epoch 1/2... Discriminator Loss: 0.3791... Generator Loss: 1.7901... TS: 2017-08-23 15:34:44.012353
Epoch 1/2... Discriminator Loss: 1.7128... Generator Loss: 0.2590... TS: 2017-08-23 15:34:47.290829
Epoch 1/2... Discriminator Loss: 0.8462... Generator Loss: 0.7773... TS: 2017-08-23 15:34:51.070068
Epoch 1/2... Discriminator Loss: 0.5427... Generator Loss: 1.1551... TS: 2017-08-23 15:34:54.371955
Epoch 1/2... Discriminator Loss: 1.8615... Generator Loss: 0.2124... TS: 2017-08-23 15:34:57.977037
Epoch 1/2... Discriminator Loss: 0.5472... Generator Loss: 1.2112... TS: 2017-08-23 15:35:01.284259
Epoch 1/2... Discriminator Loss: 1.6725... Generator Loss: 0.3102... TS: 2017-08-23 15:35:05.080539
Epoch 1/2... Discriminator Loss: 0.4834... Generator Loss: 1.1910... TS: 2017-08-23 15:35:08.260572
Epoch 1/2... Discriminator Loss: 0.0677... Generator Loss: 3.2197... TS: 2017-08-23 15:35:11.923967
Epoch 1/2... Discriminator Loss: 0.7650... Generator Loss: 0.9127... TS: 2017-08-23 15:35:15.126772
Epoch 1/2... Discriminator Loss: 0.1783... Generator Loss: 2.9031... TS: 2017-08-23 15:35:18.875997
Epoch 1/2... Discriminator Loss: 0.6579... Generator Loss: 0.9493... TS: 2017-08-23 15:35:22.090683
Epoch 1/2... Discriminator Loss: 0.9599... Generator Loss: 0.7476... TS: 2017-08-23 15:35:25.841141
Epoch 1/2... Discriminator Loss: 0.3007... Generator Loss: 1.8301... TS: 2017-08-23 15:35:29.022011
Epoch 1/2... Discriminator Loss: 1.0767... Generator Loss: 0.6139... TS: 2017-08-23 15:35:32.676296
Epoch 1/2... Discriminator Loss: 0.1425... Generator Loss: 4.4020... TS: 2017-08-23 15:35:36.203286
Epoch 2/2... Discriminator Loss: 0.7189... Generator Loss: 2.4809... TS: 2017-08-23 15:35:42.001621
Epoch 2/2... Discriminator Loss: 0.4098... Generator Loss: 1.5640... TS: 2017-08-23 15:35:45.262114
Epoch 2/2... Discriminator Loss: 0.3349... Generator Loss: 1.7115... TS: 2017-08-23 15:35:49.101480
Epoch 2/2... Discriminator Loss: 0.2406... Generator Loss: 2.5084... TS: 2017-08-23 15:35:52.378564
Epoch 2/2... Discriminator Loss: 1.4780... Generator Loss: 0.3514... TS: 2017-08-23 15:35:56.095571
Epoch 2/2... Discriminator Loss: 0.4385... Generator Loss: 1.6132... TS: 2017-08-23 15:35:59.468154
Epoch 2/2... Discriminator Loss: 1.3070... Generator Loss: 0.4232... TS: 2017-08-23 15:36:03.266064
Epoch 2/2... Discriminator Loss: 0.4984... Generator Loss: 1.2902... TS: 2017-08-23 15:36:06.501918
Epoch 2/2... Discriminator Loss: 0.5422... Generator Loss: 1.2942... TS: 2017-08-23 15:36:10.351588
Epoch 2/2... Discriminator Loss: 0.2978... Generator Loss: 1.8006... TS: 2017-08-23 15:36:13.562487
Epoch 2/2... Discriminator Loss: 0.5597... Generator Loss: 1.2440... TS: 2017-08-23 15:36:17.352073
Epoch 2/2... Discriminator Loss: 0.1487... Generator Loss: 2.4611... TS: 2017-08-23 15:36:20.548113
Epoch 2/2... Discriminator Loss: 0.7960... Generator Loss: 0.9654... TS: 2017-08-23 15:36:24.240681
Epoch 2/2... Discriminator Loss: 0.7414... Generator Loss: 1.1285... TS: 2017-08-23 15:36:27.503706
Epoch 2/2... Discriminator Loss: 0.6620... Generator Loss: 1.0242... TS: 2017-08-23 15:36:31.166341
Epoch 2/2... Discriminator Loss: 0.6399... Generator Loss: 1.0962... TS: 2017-08-23 15:36:34.377922
Epoch 2/2... Discriminator Loss: 0.3859... Generator Loss: 3.4186... TS: 2017-08-23 15:36:38.053254
Epoch 2/2... Discriminator Loss: 0.7234... Generator Loss: 1.0003... TS: 2017-08-23 15:36:41.245509
Epoch 2/2... Discriminator Loss: 0.8365... Generator Loss: 0.8218... TS: 2017-08-23 15:36:45.010113
Epoch 2/2... Discriminator Loss: 0.3190... Generator Loss: 1.7838... TS: 2017-08-23 15:36:48.297164
Epoch 2/2... Discriminator Loss: 0.8631... Generator Loss: 0.7122... TS: 2017-08-23 15:36:52.242848
Epoch 2/2... Discriminator Loss: 2.8423... Generator Loss: 0.0912... TS: 2017-08-23 15:36:55.526673
Epoch 2/2... Discriminator Loss: 1.3456... Generator Loss: 3.0199... TS: 2017-08-23 15:36:59.285779
Epoch 2/2... Discriminator Loss: 0.2043... Generator Loss: 2.8957... TS: 2017-08-23 15:37:02.655345
Epoch 2/2... Discriminator Loss: 1.2822... Generator Loss: 0.4601... TS: 2017-08-23 15:37:06.415325
Epoch 2/2... Discriminator Loss: 0.4918... Generator Loss: 1.3171... TS: 2017-08-23 15:37:09.632274
Epoch 2/2... Discriminator Loss: 2.4112... Generator Loss: 0.1554... TS: 2017-08-23 15:37:14.473915
Epoch 2/2... Discriminator Loss: 1.1250... Generator Loss: 0.5960... TS: 2017-08-23 15:37:17.781792
Epoch 2/2... Discriminator Loss: 0.1221... Generator Loss: 2.8879... TS: 2017-08-23 15:37:21.515331
Epoch 2/2... Discriminator Loss: 1.3311... Generator Loss: 0.4796... TS: 2017-08-23 15:37:24.789317
Epoch 2/2... Discriminator Loss: 0.3542... Generator Loss: 1.8440... TS: 2017-08-23 15:37:28.555529
Epoch 2/2... Discriminator Loss: 0.8646... Generator Loss: 0.9688... TS: 2017-08-23 15:37:31.845712
Epoch 2/2... Discriminator Loss: 1.9735... Generator Loss: 0.2863... TS: 2017-08-23 15:37:35.643947
Epoch 2/2... Discriminator Loss: 1.5612... Generator Loss: 0.3295... TS: 2017-08-23 15:37:38.927593
Epoch 2/2... Discriminator Loss: 0.8948... Generator Loss: 0.7142... TS: 2017-08-23 15:37:42.732338
Epoch 2/2... Discriminator Loss: 0.9762... Generator Loss: 0.6317... TS: 2017-08-23 15:37:45.955869
Epoch 2/2... Discriminator Loss: 0.6697... Generator Loss: 1.7439... TS: 2017-08-23 15:37:49.724452
Epoch 2/2... Discriminator Loss: 1.0562... Generator Loss: 0.6382... TS: 2017-08-23 15:37:52.968373
Epoch 2/2... Discriminator Loss: 0.4178... Generator Loss: 1.6493... TS: 2017-08-23 15:37:56.746099
Epoch 2/2... Discriminator Loss: 0.5375... Generator Loss: 2.7990... TS: 2017-08-23 15:38:00.173342
Epoch 2/2... Discriminator Loss: 0.4763... Generator Loss: 1.3227... TS: 2017-08-23 15:38:04.048154
Epoch 2/2... Discriminator Loss: 0.6570... Generator Loss: 1.0350... TS: 2017-08-23 15:38:07.256607
Epoch 2/2... Discriminator Loss: 0.5595... Generator Loss: 1.1543... TS: 2017-08-23 15:38:11.167335
Epoch 2/2... Discriminator Loss: 0.4729... Generator Loss: 1.2893... TS: 2017-08-23 15:38:14.561698
Epoch 2/2... Discriminator Loss: 0.5359... Generator Loss: 1.3033... TS: 2017-08-23 15:38:18.252937
Epoch 2/2... Discriminator Loss: 0.2528... Generator Loss: 1.8345... TS: 2017-08-23 15:38:21.416689
Epoch 2/2... Discriminator Loss: 1.0720... Generator Loss: 0.5476... TS: 2017-08-23 15:38:25.255380
Epoch 2/2... Discriminator Loss: 0.3864... Generator Loss: 1.6480... TS: 2017-08-23 15:38:28.565552
Epoch 2/2... Discriminator Loss: 0.0528... Generator Loss: 4.6261... TS: 2017-08-23 15:38:32.335595
Epoch 2/2... Discriminator Loss: 0.9282... Generator Loss: 0.7577... TS: 2017-08-23 15:38:35.608663
Epoch 2/2... Discriminator Loss: 0.2303... Generator Loss: 2.1890... TS: 2017-08-23 15:38:39.320028
Epoch 2/2... Discriminator Loss: 0.3010... Generator Loss: 1.8986... TS: 2017-08-23 15:38:42.551444
Epoch 2/2... Discriminator Loss: 0.4990... Generator Loss: 1.3715... TS: 2017-08-23 15:38:46.307595
Epoch 2/2... Discriminator Loss: 0.3784... Generator Loss: 1.4534... TS: 2017-08-23 15:38:49.539306
Epoch 2/2... Discriminator Loss: 1.3738... Generator Loss: 0.4108... TS: 2017-08-23 15:38:53.416840
Epoch 2/2... Discriminator Loss: 0.6483... Generator Loss: 1.0901... TS: 2017-08-23 15:38:56.641420
Epoch 2/2... Discriminator Loss: 0.9191... Generator Loss: 0.7454... TS: 2017-08-23 15:39:00.393366
Epoch 2/2... Discriminator Loss: 0.8049... Generator Loss: 0.8232... TS: 2017-08-23 15:39:03.605748
Epoch 2/2... Discriminator Loss: 0.4671... Generator Loss: 1.3979... TS: 2017-08-23 15:39:07.285042
Epoch 2/2... Discriminator Loss: 1.9277... Generator Loss: 0.2251... TS: 2017-08-23 15:39:10.493537
Epoch 2/2... Discriminator Loss: 0.3745... Generator Loss: 2.0739... TS: 2017-08-23 15:39:14.188281
Epoch 2/2... Discriminator Loss: 1.0622... Generator Loss: 0.5847... TS: 2017-08-23 15:39:17.370417
Epoch 2/2... Discriminator Loss: 1.1051... Generator Loss: 1.5731... TS: 2017-08-23 15:39:21.256071
Epoch 2/2... Discriminator Loss: 0.3635... Generator Loss: 1.8750... TS: 2017-08-23 15:39:24.516122
Epoch 2/2... Discriminator Loss: 0.2814... Generator Loss: 1.8781... TS: 2017-08-23 15:39:28.328082
Epoch 2/2... Discriminator Loss: 1.6274... Generator Loss: 0.3358... TS: 2017-08-23 15:39:31.609332
Epoch 2/2... Discriminator Loss: 0.8077... Generator Loss: 0.7564... TS: 2017-08-23 15:39:35.362254
Epoch 2/2... Discriminator Loss: 0.9169... Generator Loss: 0.8152... TS: 2017-08-23 15:39:38.586044
Epoch 2/2... Discriminator Loss: 0.1706... Generator Loss: 2.4177... TS: 2017-08-23 15:39:42.357832
Epoch 2/2... Discriminator Loss: 0.8043... Generator Loss: 0.8668... TS: 2017-08-23 15:39:45.606074
Epoch 2/2... Discriminator Loss: 0.6638... Generator Loss: 1.1502... TS: 2017-08-23 15:39:49.471036
Epoch 2/2... Discriminator Loss: 0.7821... Generator Loss: 0.8498... TS: 2017-08-23 15:39:52.651368
Epoch 2/2... Discriminator Loss: 0.3919... Generator Loss: 2.3968... TS: 2017-08-23 15:39:56.571559
Epoch 2/2... Discriminator Loss: 0.2230... Generator Loss: 2.2945... TS: 2017-08-23 15:39:59.832470
Epoch 2/2... Discriminator Loss: 0.1270... Generator Loss: 3.1756... TS: 2017-08-23 15:40:03.651207
Epoch 2/2... Discriminator Loss: 0.9287... Generator Loss: 0.6236... TS: 2017-08-23 15:40:06.927218
Epoch 2/2... Discriminator Loss: 0.2496... Generator Loss: 2.3321... TS: 2017-08-23 15:40:10.623618
Epoch 2/2... Discriminator Loss: 0.9330... Generator Loss: 0.7647... TS: 2017-08-23 15:40:13.793667
Epoch 2/2... Discriminator Loss: 0.7412... Generator Loss: 0.9577... TS: 2017-08-23 15:40:17.516541
Epoch 2/2... Discriminator Loss: 1.4735... Generator Loss: 0.3468... TS: 2017-08-23 15:40:20.715908
Epoch 2/2... Discriminator Loss: 0.1620... Generator Loss: 2.5711... TS: 2017-08-23 15:40:24.583058
Epoch 2/2... Discriminator Loss: 0.2773... Generator Loss: 1.9287... TS: 2017-08-23 15:40:28.001082
Epoch 2/2... Discriminator Loss: 0.5820... Generator Loss: 1.0689... TS: 2017-08-23 15:40:31.768484
Epoch 2/2... Discriminator Loss: 1.6037... Generator Loss: 0.3357... TS: 2017-08-23 15:40:35.108464
Epoch 2/2... Discriminator Loss: 2.1770... Generator Loss: 0.1711... TS: 2017-08-23 15:40:39.016508
Epoch 2/2... Discriminator Loss: 0.9767... Generator Loss: 0.7841... TS: 2017-08-23 15:40:42.371060
Epoch 2/2... Discriminator Loss: 0.0909... Generator Loss: 3.6630... TS: 2017-08-23 15:40:46.153074
Epoch 2/2... Discriminator Loss: 0.1646... Generator Loss: 2.8470... TS: 2017-08-23 15:40:49.430990
Epoch 2/2... Discriminator Loss: 1.1050... Generator Loss: 0.6707... TS: 2017-08-23 15:40:53.118746
Epoch 2/2... Discriminator Loss: 0.3273... Generator Loss: 1.6704... TS: 2017-08-23 15:40:56.401694
Epoch 2/2... Discriminator Loss: 0.3751... Generator Loss: 1.4634... TS: 2017-08-23 15:41:00.279568
Epoch 2/2... Discriminator Loss: 0.2863... Generator Loss: 2.0467... TS: 2017-08-23 15:41:03.638314
Epoch 2/2... Discriminator Loss: 0.1751... Generator Loss: 2.6789... TS: 2017-08-23 15:41:07.459182
Epoch 2/2... Discriminator Loss: 0.6479... Generator Loss: 1.0625... TS: 2017-08-23 15:41:10.797592
Epoch 2/2... Discriminator Loss: 1.2370... Generator Loss: 0.4672... TS: 2017-08-23 15:41:14.517011
Epoch 2/2... Discriminator Loss: 1.6834... Generator Loss: 0.2720... TS: 2017-08-23 15:41:17.679220
Epoch 2/2... Discriminator Loss: 1.0681... Generator Loss: 0.8993... TS: 2017-08-23 15:41:21.410232
Epoch 2/2... Discriminator Loss: 0.7663... Generator Loss: 0.9267... TS: 2017-08-23 15:41:24.637368
Epoch 2/2... Discriminator Loss: 0.2741... Generator Loss: 1.9191... TS: 2017-08-23 15:41:28.485537
Epoch 2/2... Discriminator Loss: 0.8684... Generator Loss: 0.7809... TS: 2017-08-23 15:41:31.719609
Epoch 2/2... Discriminator Loss: 0.4725... Generator Loss: 1.3721... TS: 2017-08-23 15:41:35.517880
Epoch 2/2... Discriminator Loss: 0.9588... Generator Loss: 0.6525... TS: 2017-08-23 15:41:38.797156
Epoch 2/2... Discriminator Loss: 0.2457... Generator Loss: 2.1934... TS: 2017-08-23 15:41:42.538102
Epoch 2/2... Discriminator Loss: 0.5957... Generator Loss: 1.0638... TS: 2017-08-23 15:41:45.954041
Epoch 2/2... Discriminator Loss: 0.3479... Generator Loss: 1.6784... TS: 2017-08-23 15:41:49.743154
Epoch 2/2... Discriminator Loss: 0.7535... Generator Loss: 0.7857... TS: 2017-08-23 15:41:52.907384
Epoch 2/2... Discriminator Loss: 0.9961... Generator Loss: 0.5625... TS: 2017-08-23 15:41:56.771911
Epoch 2/2... Discriminator Loss: 1.2182... Generator Loss: 0.5428... TS: 2017-08-23 15:42:00.034503
Epoch 2/2... Discriminator Loss: 2.5022... Generator Loss: 0.1008... TS: 2017-08-23 15:42:03.819506
Epoch 2/2... Discriminator Loss: 0.5378... Generator Loss: 0.9887... TS: 2017-08-23 15:42:07.044619
Epoch 2/2... Discriminator Loss: 0.9556... Generator Loss: 0.6931... TS: 2017-08-23 15:42:10.783662
Epoch 2/2... Discriminator Loss: 0.8359... Generator Loss: 0.7145... TS: 2017-08-23 15:42:14.049241
Epoch 2/2... Discriminator Loss: 1.4060... Generator Loss: 0.4205... TS: 2017-08-23 15:42:17.781900
Epoch 2/2... Discriminator Loss: 0.5619... Generator Loss: 1.6058... TS: 2017-08-23 15:42:21.017536
Epoch 2/2... Discriminator Loss: 1.4929... Generator Loss: 0.3925... TS: 2017-08-23 15:42:24.913052
Epoch 2/2... Discriminator Loss: 1.0695... Generator Loss: 0.5328... TS: 2017-08-23 15:42:28.130609
Epoch 2/2... Discriminator Loss: 0.4165... Generator Loss: 1.4366... TS: 2017-08-23 15:42:31.928085
Epoch 2/2... Discriminator Loss: 0.2526... Generator Loss: 2.0929... TS: 2017-08-23 15:42:35.144254
Epoch 2/2... Discriminator Loss: 0.1805... Generator Loss: 2.2401... TS: 2017-08-23 15:42:39.026972
Epoch 2/2... Discriminator Loss: 0.2622... Generator Loss: 1.9984... TS: 2017-08-23 15:42:42.320454
Epoch 2/2... Discriminator Loss: 0.7277... Generator Loss: 0.9615... TS: 2017-08-23 15:42:46.095797
Epoch 2/2... Discriminator Loss: 1.1942... Generator Loss: 0.4897... TS: 2017-08-23 15:42:49.359195
Epoch 2/2... Discriminator Loss: 1.3331... Generator Loss: 0.4354... TS: 2017-08-23 15:42:53.243462
Epoch 2/2... Discriminator Loss: 0.8856... Generator Loss: 0.7066... TS: 2017-08-23 15:42:56.415414
Epoch 2/2... Discriminator Loss: 0.5930... Generator Loss: 1.1832... TS: 2017-08-23 15:43:00.163170
Epoch 2/2... Discriminator Loss: 0.4268... Generator Loss: 1.1894... TS: 2017-08-23 15:43:03.400100

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.